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Assembling appropriate FAS 5 (ASC 450-20) pools

Sageworks
March 25, 2013
Read Time: 0 min

In an earlier video, we discussed some of the common challenges regarding the allowance for loan and lease losses processes. In today’s video, Sageworks discusses specific challenges related to the FAS 5 pools.

 

From the video

In terms of the pooled loan analysis, in the past it has been acceptable for community banks to use fewer, more generalized loan pools and for institutions to use rules of thumb to decide loss rates based on risk ratings, (assigning 1, 5, 15 for Pass, Special Mention, and Substandard as an example). Now regulators are stricter in enforcing a defensible methodology that utilizes a greater level of granularity in loan pools, based on groups of loans with similar risk characteristics. For example, within real estate, there can be several different pools broken out by property type, lien position, owner-occupied status, vintage, etc. 

Depending on an individual bank’s portfolio compositions, getting the appropriate groupings in terms of loans with similar risk is important. However, it is also possible for banks to be too granular, so the institution needs to be careful in evaluating the different levels of risk in their portfolio when assembling their loan pools. 

The question of whether to sub-segment or not often arises, and the ASU 2010-20 and other guidance have mentioned adding a third level of disaggregation. When you are putting together segmentation using 1, 2 and 3 attributes (optional) the third attribute could include the risk classification or delinquency bucket, among other factors. You can also account for levels of risk without sub- segmenting your portfolio but by tracking and accounting for changes in the levels of watch list or classified loans within a FAS 5 pool and accounting for this through the qualitative and environmental factors.

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About the Author

Sageworks

Raleigh, N.C.-based Sageworks, a leading provider of lending, credit risk, and portfolio risk software that enables banks and credit unions to efficiently grow and improve the borrower experience, was founded in 1998. Using its platform, Sageworks analyzed over 11.5 million loans, aggregated the corresponding loan data, and created the largest

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